The heteronomy of algorithms: Traditional knowledge and computational knowledge
- URL: http://arxiv.org/abs/2505.11030v1
- Date: Fri, 16 May 2025 09:25:00 GMT
- Title: The heteronomy of algorithms: Traditional knowledge and computational knowledge
- Authors: David M. Berry,
- Abstract summary: I argue that we must begin teaching the principles of critiquing the computal through new notions of what we might call digital Bildung.<n>Not only is there a need to raise the intellectual tone regarding computation and its related softwarization processes, but there is an urgent need to attend to the likely challenges from computation.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: If an active citizen should increasingly be a computationally enlightened one, replacing the autonomy of reason with the heteronomy of algorithms, then I argue in this article that we must begin teaching the principles of critiquing the computal through new notions of what we might call digital Bildung. Indeed, if civil society itself is mediated by computational systems and media, the public use of reason must also be complemented by skills for negotiating and using these computal forms to articulate such critique. Not only is there a need to raise the intellectual tone regarding computation and its related softwarization processes, but there is an urgent need to attend to the likely epistemic challenges from computation which, as presently constituted, tends towards justification through a philosophy of utility rather than through a philosophy of care for the territory of the intellect. We therefore need to develop an approach to this field that uses concepts and methods drawn from philosophy, politics, history, anthropology, sociology, media studies, computer science, and the humanities more generally, to try to understand these issues - particularly the way in which software and data increasingly penetrate our everyday life and the pressures and fissures that are created. We must, in other words, move to undertake a critical interdisciplinary research program to understand the way in which these systems are created, instantiated, and normatively engendered in both specific and general contexts.
Related papers
- Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence [59.07578850674114]
Sound deductive reasoning is an indisputably desirable aspect of general intelligence.<n>It is well-documented that even the most advanced frontier systems regularly and consistently falter on easily-solvable reasoning tasks.<n>We argue that their unsound behavior is a consequence of the statistical learning approach powering their development.
arXiv Detail & Related papers (2025-06-30T14:37:50Z) - Synthetic media and computational capitalism: towards a critical theory of artificial intelligence [0.0]
I argue that we need new critical methods capable of addressing both the technical specificity of AI systems and their role in restructuring forms of life under computational capitalism.<n>The paper concludes by suggesting that critical reflexivity is needed to engage with the algorithmic condition without being subsumed by it.
arXiv Detail & Related papers (2025-03-22T22:59:28Z) - Open Problems in Mechanistic Interpretability [61.44773053835185]
Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks' capabilities.<n>Despite recent progress toward these goals, there are many open problems in the field that require solutions.
arXiv Detail & Related papers (2025-01-27T20:57:18Z) - Political-LLM: Large Language Models in Political Science [159.95299889946637]
Large language models (LLMs) have been widely adopted in political science tasks.<n>Political-LLM aims to advance the comprehensive understanding of integrating LLMs into computational political science.
arXiv Detail & Related papers (2024-12-09T08:47:50Z) - Public Computing Intellectuals in the Age of AI Crisis [0.0]
This position paper endeavors to do so in four sections.
The first explores what is at stake for computing in the narrative of an AI crisis.
The second articulates possible educational responses to this crisis.
The third section presents a novel characterization of academic computing's field of practice.
arXiv Detail & Related papers (2024-05-01T20:48:34Z) - A Survey of Deep Learning for Mathematical Reasoning [71.88150173381153]
We review the key tasks, datasets, and methods at the intersection of mathematical reasoning and deep learning over the past decade.
Recent advances in large-scale neural language models have opened up new benchmarks and opportunities to use deep learning for mathematical reasoning.
arXiv Detail & Related papers (2022-12-20T18:46:16Z) - Scientia Potentia Est -- On the Role of Knowledge in Computational
Argumentation [52.903665881174845]
We propose a pyramid of types of knowledge required in computational argumentation.
We briefly discuss the state of the art on the role and integration of these types in the field.
arXiv Detail & Related papers (2021-07-01T08:12:41Z) - Do Abstractions Have Politics? Toward a More Critical Algorithm Analysis [19.08810272234958]
We argue for affordance analysis, a more critical algorithm analysis based on an affordance account of value embedding.
We illustrate 5 case studies of how affordance analysis refutes social determination of technology.
arXiv Detail & Related papers (2021-01-04T05:59:26Z) - Interdisciplinary Approaches to Understanding Artificial Intelligence's
Impact on Society [7.016365171255391]
AI has come with a storm of unanticipated socio-technical problems.
We need tighter coupling of computer science and those disciplines that study society and societal values.
arXiv Detail & Related papers (2020-12-11T00:43:47Z) - A Data-Driven Study of Commonsense Knowledge using the ConceptNet
Knowledge Base [8.591839265985412]
Acquiring commonsense knowledge and reasoning is recognized as an important frontier in achieving general Artificial Intelligence (AI)
In this paper, we propose and conduct a systematic study to enable a deeper understanding of commonsense knowledge by doing an empirical and structural analysis of the ConceptNet knowledge base.
Detailed experimental results on three carefully designed research questions, using state-of-the-art unsupervised graph representation learning ('embedding') and clustering techniques, reveal deep substructures in ConceptNet relations.
arXiv Detail & Related papers (2020-11-28T08:08:25Z) - Neuro-symbolic Architectures for Context Understanding [59.899606495602406]
We propose the use of hybrid AI methodology as a framework for combining the strengths of data-driven and knowledge-driven approaches.
Specifically, we inherit the concept of neuro-symbolism as a way of using knowledge-bases to guide the learning progress of deep neural networks.
arXiv Detail & Related papers (2020-03-09T15:04:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.